How B2B Sales Analytics Surge Revenue Growth


  • The goal of B2B sales analytics is to get the right information to the right people at the right time, so they make more informed and better decisions.
  • Most sellers are data rich and information poor. They understand the value of data but struggle to transform it into actionable intelligence. The incur a data paradox where data is a valuable but underutilized asset.
  • B2B sales analytics drive real-time and continuous improvements to performance. Even small improvements are shown to deliver significant financial benefits.
Johnny Grow Revenue Growth Consulting

B2B Sales Analytics Research Findings

Research published in The Sales Excellence Report found some interesting disparities when it came to B2B sales analytics.

Survey participants were asked to rate each type of analytic (on a 10-point scale) in terms of effectiveness. For those business intelligence tools rated above a score of 8 we asked why they were effective. For those scored below 7 we asked why they were ineffective.

The three most effective tools were dashboards, predictive reporting and artificial intelligence (AI). However, in an atypical data pattern, each of these tools incurred both high and low scores.

B2B Sales Analytics Effectiveness

However, the qualitative analysis brought the data into focus and suggested effectiveness was less about the technology itself and more about how it was designed and used.

Performance Dashboards

Survey participants ranked in the Best-in-Class archetype were 1.8X more likely to benefit from performance dashboards than their peers, and 3X more likely to use interactive dashboards.

Interactive Dashboards

Sales dashboards rated as ineffective were described as all show and no go. They are visually appealing but fail to gain user adoption as they don't really display anything that helps the seller improve his or her performance.

On the flip side, dashboards rated as effective were less about displaying content and more about making the information actionable.

The five most effective ways to make data actionable include the following:

  1. Make the metrics highly visual and interactive
  2. Align departmental metrics with the most important company outcomes
  3. Show KPIs alongside budgets or industry benchmarks for context
  4. Allow the data to be interrogated, manipulated and used for predictive analytics, and
  5. Link the data findings to recommended actions such as a Playbook

The below dashboard we created for a client is an example of how to make data actionable. Negative variances are color coded, offer drill-down analysis and display links to a Sales Playbook for prescriptive recommendations to remedy each shortfall.

Sales Dashboard

Predictive Analytics

The use of predictive analytics was the single greatest disparity among Best-in-Class revenue growth leaders and their lower performing peers.

Predictive Analytics

Research shows that Best-in-Class leaders use predictive analytics 4 times more than their lower performing peers.

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Most revenue reporting displays historical data. Better reporting shifts from lagging to leading indicators. And the best reporting enables metrics to be interactive, so sellers can perform What-If modeling and scenario planning.

Inserting performance benchmarks creates additional intelligence. When company performance metrics are compared with industry peers management can quickly spot gaps or underperforming areas that offer the biggest financial uplift.

Sales Win Rate Benchmark

The combination of performance benchmarks and interactive measures permit managers and sellers to model future performance and shift their visibility from where they have been to where they are going. It's the difference between looking in your cars rearview mirror or through the windshield.

Sales Predictive Analytics

The Johnny Grow Sales Growth Framework is built on predictive analytics that identify the shortest and most profitable route to revenue growth. The below Predictive Pyramid is a holistic model extracted from our Revenue Engine that illustrates how to apply end to end predictive reporting to grow the company.

Sales Growth Predictive Model

Artificial Intelligence

Those participants that found AI ineffective indicated the technology failed to provide answers to important selling problems. Several advised that they got stuck in perpetual AI pilots because they were organized as technology projects looking for business outcomes. That seldom works as the business outcomes need to be the starting point.

CRM systems hold a treasure trove of customer and sales data. However, there is such a thing as too much data, or at least more data than can be manually processed.

AI is the technology to transition CRM software from a data depository to a predictor of customer behaviors, creator of selling insights and facilitator of customer and company objectives.

Participants that found AI effective advised that the technology shifts customer and data from reactive to proactive, elevates data from a byproduct to an asset, and creates a competitive advantage.


When sellers integrate AI into work processes, they make more timely and better decisions, and the company achieves a sustainable competitive advantage. And that is because making better and faster decisions never loses its value.

The Point is This

Business intelligence includes the tools and technologies that make revenue leaders and sellers smart.

They can advise when you need to make a timely course correction to achieve a business outcome.

For example, they surface the leads being neglected, the sale opportunities that need attention and the forecasted deals that are at risk. Advanced analytics such as AI can predict which opportunities are winnable and which are not.

Real-time distribution of variance alerts permits proactive adjustments to head off problems before they happen or remedy performance shortfalls before they exacerbate.

Real-time information reporting gives management visibility so they know where to focus their limited time to achieve the desired results.

Business intelligence is essential to measure progress, convert data into insights and deliver highly actionable recommendations.